In 1827, plant biologist Robert Brown discovered what is known as Brownian motion, a class of (mathematically defined) chaos. The formal derivative of Brownian motion is Gaussian white-noise. In 1958, Nobert Wiener proposed to use the Gaussian white-noise as an input probe to identify a system by a series of orthogonal functionals, the Wiener G-functionals. Our past studies have shown that white-noise analysis is uniquely suited for studying the response dynamics of the retinal neuron network. Here we propose to refine further this analytical tool by taking advantage of recent theoretical developments. Newer methodology will be applied to study signal transformation in the vertebrate inner retinal network as well as information compression taking place in the ganglion cells where the analog signals are translated into a point process, spike discharges.